Online peer assessment: effects of cognitive and affectivefeedback

Jingyan Lu • Nancy Law

Received: 19 January 2011 / Accepted: 24 June 2011 / Published online: 5 July 2011� The Author(s) 2011. This article is published with open access at Springerlink.com

Abstract This study reports the effects of online peer assessment, in the form of peer

grading and peer feedback, on students’ learning. One hundred and eighty one high school

students engaged in peer assessment via an online system—iLap. The number of grade-

giving and grade-receiving experiences was examined and the peer feedback was coded

according to different cognitive and affective dimensions. The effects, on both assessors

and assessees, were analyzed using multiple regression. The results indicate that the

provision by student assessors of feedback that identified problems and gave suggestions

was a significant predictor of the performance of the assessors themselves, and that positive

affective feedback was related to the performance of assessees. However, peer grading

behaviors were not a significant predictor of project performance. This study explains the

benefits of online peer assessment in general and highlights the importance of specific

types of feedback. Moreover, it expands our understanding of how peer assessment affects

the different parties involved.

Keywords Peer assessment � Online assessment � Peer feedback � Peer grading �Cognitive feedback � Affective feedback

Introduction

This paper reports on a study involving the design of online peer assessment activities to

support high school students’ project-based learning; the study examined the effects of

different types of peer assessment on student learning. Assessment has an important

influence on the strategies, motivation, and learning outcomes of students (Crooks 1988).

Traditionally, too much emphasis has been placed on assessment by grading, and too little

on assessment as a way of helping students learn (Crooks 1988; Stiggins 2002). In recent

years, peer assessment has been adopted as a strategy for ‘‘formative assessment’’ (Cheng

and Warren 1999; Sadler 1989) or ‘‘assessment for learning’’ and for involving students as

J. Lu (&) � N. LawFaculty of Education, The University of Hong Kong, Pokfulam Road, Hong Kong, Chinae-mail: jingyan@hku.hk

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Instr Sci (2012) 40:257–275DOI 10.1007/s11251-011-9177-2

active learners (Gielen et al. 2009; Sadler 1989; Topping et al. 2000). Further, research on

peer assessment has amassed substantial evidence on the cognitive (Nelson and Schunn

2009; Tseng and Tsai 2007), pedagogical (Falchikov and Blythman 2001), meta-cognitive

(Butler and Winne 1995; Topping 1998), and affective benefits (Strijbos et al. 2010) of

peer assessment on student learning (Topping 2003). These efforts have resulted in peer

assessment being successfully designed and implemented in K-12 classrooms and in

higher-education contexts (Topping 2003).

Online assessment has become increasingly popular since the advent of the Internet and

has significantly changed the process of assessment (Tseng and Tsai 2007). Online systems

introduce such functions as assignment submission, storage, communication and review

management (Kwok and Ma 1999; Liu et al. 2001), and online assessment has a number of

advantages over face-to-face assessment (Tsai 2009; Tsai and Liang 2009; Yang and Tsai

2010). Online assessment enables students to communicate with peers and to reflect on and

continuously revise their work based on feedback (Yang 2010). Online systems can

increase the willingness of students to engage in peer assessment by allowing them to

anonymously grade and provide feedback when and where they like (Lin et al. 2001; Tsai

2009). These systems also allow teachers to monitor the online activities and progress of

their students more closely (Lin et al. 2001), and enable researchers to collect information

about students by automatically recording data about assignments, online participation and

communication (Tsai 2009). Finally, teachers can automatically assign students to review

more heterogeneous or homogeneous work based on background features such as gender,

achievement, and preferences (Tsai 2009).

The highly variable nature of peer assessment practices makes it difficult to determine

their effects on learning (van Gennip et al. 2010). Further, as research has tended to focus

on university students, we have an under-representation in the literature of these practices

in schools. Thus an investigation of the peer assessment practices of high school students

will help rectify this unfortunate situation and enable their practices to be compared with

those of university students.

This study focuses on how peer grading and peer feedback affect the performance of

both assessors and assessees. The paper starts with a discussion of two important assess-

ment components: peer grading and peer feedback. Next, the features of the online

assessment system and peer assessment procedures are reported, and the research questions

are introduced. This is followed by a description and discussion of the methods of data

collection and data analysis adopted. Finally, there is a discussion of the major findings,

implications of this study and directions for future work.

Literature review

Many teachers confuse assessment with grading and consider them to be the same thing.

Although grading can be seen as a form of assessment, assessment does not necessarily

involve grading. This study treats peer grading and peer feedback as two forms of peer

assessment and examines their effects on the learning of assessors and assessees.

Peer grading

In peer grading, assessors apply criteria for assigning grades to the work of their peers;

many studies have shown this to be a reliable and valid approach (Falchikov and Goldfinch

2000). For instance, a meta-analysis shows a mean correlation of 0.69 between peer- and

258 J. Lu, N. Law

123

teacher- assigned grades, indicating that peer assessment can be reliable (Falchikov and

Goldfinch 2000). This finding can perhaps be explained by the fact that teachers often

support peer grading by providing students with assessment rubrics to ensure consistent

and reliable peer evaluations (Jonsson and Svingby 2007). Assessment rubrics based on

descriptive scales show students what is important in assignments and specify the strengths

and weaknesses of the work under evaluation (Andrade 2000; Moskal 2000). By applying

rubrics to the work of peers, assessors enhance their awareness and understanding of the

assessment criteria and, as a result, are likely to apply these to their own work more

reflectively and attentively.

Studies show that rubric-supported peer grading enhances student learning. Students

become more reflective and their learning outcomes improve when they are involved in

defining marking rubrics (Stefani 1994). Reports from undergraduates have indicated that,

although peer grading is challenging and time-consuming, it is also beneficial as it enables

students to think more critically and to learn more effectively (Falchikov 1986; Hughes

1995; Orsmond et al. 1996). While peer grading is widely used and its effects on university

students have been extensively examined, its effects on high school students have seldom

been investigated.

Peer feedback

Despite the positive reports on the impact of peer grading on students’ learning, many

researchers have argued that evaluative feedback is more important than assigning grades

(Ellman 1975; Liu and Carless 2006) and that peer feedback is more effective than grading

in peer assessment.

Peer feedback refers to giving comments on the work or performance of peers, which

involves reflective engagement (Falchikov and Blythman 2001). Some researchers have

argued that peer feedback enhances learning by enabling learners to identify their strengths

and weaknesses, and by receiving concrete ideas on how to improve their work (Crooks

1988; Rowntree 1987; Xiao and Lucking 2008). Although, peer feedback may not achieve

the quality of teacher feedback, it can often be given in a more timely manner, more

frequently, and more voluminously (Topping 1998). Assessees may also see feedback from

peers as less threatening and so perhaps be more willing to accept it (Ellman 1975).

Cognitive and affective feedback

Nelson and Schunn (2009) differentiate feedback into cognitive and affective categories.

Cognitive feedback targets the content of the work and involves summarizing, specifying

and explaining aspects of the work under review. Affective feedback targets the quality

of works and uses affective language to bestow praise (‘‘well written’’) and criticism

(‘‘badly written’’), or uses non-verbal expressions, such as facial expressions, gestures and

emotional tones. In online learning environments, emoticons are often used for giving

affective feedback.

The effects of cognitive feedback vary with the types given or received, the nature of

the task, the developmental stage of the learners, and mediating conditions. When assessors

give cognitive feedback they summarize arguments, identify problems, offer solutions, and

explicate comments. Different forms of cognitive feedback can affect the performance of

assessees in different ways. Earlier feedback is more effective than later feedback, and

feedback to correct responses is more effective that feedback to incorrect ones (Hattie and

Timperley 2007). Specific comments are more effective than general ones (Ferris 1997),

Online peer assessment 259

123

as are timely comments emphasizing personal progress in mastering tasks (Crooks 1988).

Short, to-the-point explanations are more effective (Bitchener et al. 2005) than lengthy,

didactic ones (Tseng and Tsai 2007). Finally, comments that identify problems and provide

solutions are especially effective (Nelson and Schunn 2009). Whereas peer feedback

studies have overwhelmingly focused on the effects of written feedback on the writing

assignments of university students (either as assessors or as assessees), this study focuses

on the effects of online feedback on high school students, both assessors and assessees,

within Liberal Study projects.

Praise is usually recommended and is found to be one of the most common features

present in the feedback provided by undergraduates (Cho et al. 2006; Nilson 2003; Sadler

and Good 2006). There are many studies of praise given by teachers (Brophy 1981;

Burnett 2002; Elawar and Corno 1985; Elwell and Tiberio 1994; Page 1958; White and

Jones 2000; Wilkinson 1981), but few of them focus on praise that is given by peers.

More importantly, the effects of praise on learning are unclear and highly debated. Some

studies report positive effects of praise: high school students report that online positive

feedback significantly contributes to their learning (Tseng and Tsai 2007); first year

college students respond favorably to praise in their writing performance where it helps

them improve work quality (Straub 1997); and college students generally perceive

positive comments as motivating (Duijnhouwer 2010). Conversely, some literature indi-

cates that praise is ineffective for task performance, either for students at high school

level (Crooks 1988), or at college level (Ferris 1997), especially where the required

performance is a cognitively demanding task (Ferris 1997; Hattie and Timperley 2007;

Kluger and DeNisi 1996). Some researchers argue that the constructive effects of

affective feedback, whether positive or negative, are limited because they do not address

the cognitive content of the work under review. Hattie and Timperley (2007), for instance,

find that affective feedback fails to bring about greater task engagement and under-

standing because it usually provides little task-related information. Surprisingly, negative

effects of praise are found in Cho and Cho’s (2010) study, which reports university

students’ physics technical research drafts tend to be of a lower quality when students

receive more praise. Furthermore, most research focuses on the effects of praise on

assessees; little has been done on the effects on assessors, except for the work by Cho and

Cho (2010), who report that, the more positive comments students give, the more the

writing quality of their own revised drafts tends to improve. However, the coding on

praise comment is combined with other cognitive feedback, so the unique effect of praise

is hard to differentiate. Most research on praise is conducted in traditional classroom

settings, where students receive praise from the teacher; reports on the effects of feedback

from peers given through online systems are limited. In this study we investigate the

effects of praise and cognitive feedback on both assessors and assessees, as students

usually play both roles in the same task in a given situation.

Effects of peer feedback on assessors and assessees

Peer feedback can improve the learning of both assessors and assessees (Li et al. 2010;

Topping and Ehly 2001; Xiao and Lucking 2008) by sharpening the critical thinking skills

of the assessors and by providing timely feedback to the assessees (Ellman 1975). It

increases the time spent thinking about, comparing, contrasting and communicating about

learning tasks (Topping 1998). Furthermore, assessors review, summarize, clarify, diag-

nose misconceived knowledge, identify missing knowledge, and consider deviations from

260 J. Lu, N. Law

123

the ideal (Van Lehn et al. 1995). Assessors who provide high quality feedback have better

learning outcomes (Li et al. 2010; Liu et al. 2001). When pre-service teachers assess each

other’s work in Tsai et al.’s study (2001), those who provide more detailed and con-

structive comments perform better than those who provide less. Topping et al. (2000) also

report positive effects of peer assessment on assessors: students involved in peer assess-

ment not only improved the quality of their own work but also developed additional

transferable skills.

Some studies provide mixed findings with regard to the usefulness of peer feedback for

assessees. Olson (1990) reports that sixth graders who received both peer and teacher

feedback produced better quality writing in their final drafts than those who received only

teacher feedback. Some students show mistrust of peer assessment. Brindley and Scoffield

(1998) report that some undergraduate students expressed concerns about the objectivity of

peer assessment due to the possibility of personal bias when comments are given by peers,

and some students considered assessment to be the sole responsibility of teachers (Brindley

and Scoffield 1998; Liu and Carless 2006; Wen and Tsai 2006).

Although research indicates that peer feedback is more beneficial to assessors than to

assessees, most of the research has focused on college students. Moreover, studies com-

paring the effects of peer feedback on assessors and assessees and on primary and sec-

ondary school students are rare.

Research questions

Despite the number of studies on peer assessment, it is difficult to pinpoint the following:

What contributes to the effects of peer assessment (van Zundert et al. 2009)? Who

benefits most from it, assessors or assessees? And what role does online assessment

play? A limited number of quantitative studies compare and measure the effects of

different types of assessment on student learning; very few have explicitly compared the

effects of peer assessment on both assessors and assessees (Cho and Cho 2010; Li et al.

2010). Researchers have also identified a number of concerns about online assessment,

especially with regard to high school students (Tsai 2009). For instance, assessors may

have insufficient prior domain knowledge to judge the work of peers or an inability to

provide neutral comments, therefore assessees may have difficulties accepting and

adapting to feedback from peers. Another possible concern is a lack of computer literacy:

both parties may lack the requisite technological skills to fully negotiate the online

systems. This study was designed to explore these issues. Students participating in this

study were provided with rubrics to help them to grade the work of their peers. The

students’ academic achievements, reflecting their prior domain knowledge and computer

skills, were collected to investigate the extent to which these factors affectend their peer

assessment behavior.

This study employed an exploratory method to investigate who will benefit from peer

assessment, how, and why. As an empirical investigation to determine whether or not

online assessment activities are related to the learning performance of high school students

it posed two questions:

1. Are peer grading activities related to the quality of the final project for both assessors

and assessees?

2. Are different types of peer cognitive and affective feedback related to the quality of

the final projects for both assessors and assessees?

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123

Methods

Subjects

All 181 thirteen- to fourteen-year-old students studying at secondary two level in a publicly

funded school in Hong Kong were invited to participate in this study. The school was

chosen as a convenient sample from schools that participated in a university-school

partnership project involving the use of an online platform in the teaching and assessment

of the Liberal Studies (LS). The students studied in five different classes at this school and

were taught by different teachers who had worked together to prepare the school-based

teaching plans for the LS syllabus. The students worked on a six-week LS project, and,

given that all five teachers followed the same teaching schedule, online activities and

assessment rubrics, the schedules of the learning tasks and progress of all five classes were

equivalent. Students were encouraged to engage in peer assessment for all subtasks so that

they could become involved in a continuous process of reflection and improvement.

Nevertheless, assessment activities were not compulsory and students had the freedom to

choose which subtask(s) to assess.

Online assessment platform and task description

This study is part of a larger project (Law et al. 2009) in which an online learning platform,

known as the Interactive Learning and Assessment Platform (iLAP), was developed to

support teaching and assessment of LS. LS is a new core course implemented in secondary

schools in Hong Kong, where students are provided ‘with opportunities to explore issues

relevant to the human condition in a wide range of contexts and enables them to understand

the contemporary world and its pluralistic nature’ (Education and Manpower Bureau

2007). The teaching of the new subject poses many challenges to high school teachers, not

only because it is new but also because it advocates an inquiry-based learning approach

that is novel to both teachers and students. The online learning platform iLAP was

developed to facilitate inquiry-based learning and to help teachers manage, support, and

assess students’ learning processes and inquiry outcomes.

The functions of the iLap platform are fourfold: (1) assignment submission, (2) rubric

development, (3) assessment implementation, and (4) performance monitoring. Students

can upload assignments to the platform. iLAP provides some sample assessment rubrics for

different kinds of learning/inquiry tasks that teachers can adopt or modify for adoption in

their own teaching practice. iLAP also allows teachers to create brand new rubrics on their

own by specifying task dimensions and rating criteria for varying levels of performance.

Figure 1 is a screen-capture of the iLAP interface that illustrates rubrics creation by

teachers: each row is a rubric dimension (or criterion) and the performance levels are

represented by the columns. The online rubric on iLAP not only supports the use of teacher

assessment but also online self assessment and peer assessment.

For peer assessment, students were divided into small groups of four or five, according

to their student ID number (so that group assignment was random). Individual assignments

submitted online were made available for review and comment by fellow group members.

The peer assessment interface of iLAP comprises two parts: a rubric area and a comment

area. When an assignment is selected, the rubric created by the teacher appears to the

assessors. For instance, the rubric in Fig. 2 has five criteria: (1) topic researchability, (2)

clarity, (3) relevance and background information, (4) methodology, and (5) writing

quality. Each criterion is further divided into four levels of quality: ‘cannot be scored’

262 J. Lu, N. Law

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(score 0), ‘needs improvement’ (score 1–3), ‘satisfactory’ (score 4–6), and ‘excellent’

(score 7–9). To assign a score to their peers, students click on drop-down menus to choose

the level of quality for each category and select the cell colors according to the assigned

score. iLAP then automatically calculates the total score for all the assessed dimensions. In

addition to peer grading assigned from rubrics, students can also comment on the assessed

assignment in the comment area.

iLAP, like many other online peer assessment systems, also allows teachers to easily

monitor student performance and progress (Lin et al. 2001). Teachers can access sum-

maries of assignment submissions and assessments of student performance (see Fig. 3).

Student work is listed with peer assessment information, including grades and comments.

While LS is a new core course for senior secondary students in Hong Kong, some

schools have chosen to introduce it to students as early as junior secondary level, so as to

provide a smooth transition for both teachers and students. The curriculum context of this

study was the subject ‘‘Liberal Study and Humanities’’, which was a new subject for the

secondary two students taking part in this study. Participants worked on a project based on

one of the three topics covered in their Humanities course in the previous semester, which

was about Consumer Education, City Transportation and Development, and City Econ-

omy. Their teachers divided the project into five subtasks: (1) collect three topic-relevant

photographs, (2) draft project plans, (3) prepare interview questions, (4) draft question-

naires, and (5) write up projects. Each subtask has a different focus. Subtask 1 focused on

finding three photographs related to the topic and formulating a research question. Subtask

2 focused on drafting a plan for an empirical investigation of the research question.

Subtask 3 focused on preparing interviews, including identifying possible interviewees and

constructing interview questions. Subtask 4 focused on drafting a questionnaire for the

investigation. Subtask 5 focused on writing up the project reports. Students could collect

empirical data through interviews or surveys with their identified informants, who could be

peers, professionals, or members of the general public, as deemed appropriate by the

students based on the research question they wanted to answer.

Fig. 1 Screenshot of rubric development on iLAP

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The teachers assigned a specific rubric for students to use in the peer assessment of each

subtask. Students were asked to grade the work of their peers in their own group based on

teacher-provided rubrics and were also asked to give comments of their own volition. As

soon as grades and comments were provided by peers, they became immediately available

to assessees through iLAP. None of the subtasks used self-assessment. The teacher’s final

grading of the project, rather than peer grading scores, formed the basis of each student’s

final score.

Data sources, coding schema, and data analysis

iLAP records how often students give and receive grades and feedback, to whom these are

sent and from whom these are received, and the content of the feedback exchanged.

Achievement scores for the Computer Literacy and Humanities course in the previous

semester were collected as control variables in the analysis.

Peer feedback was qualitatively analyzed (See Table 1 for coding scheme details and

examples). The authors collaboratively developed a coding scheme based on relevant

literature and the peer assessment context of this study. Based on Nelson and Schunn

(2009), and Tseng and Tsai (2007), comments were first coded as affective and/or

cognitive. Affective comments were further coded as positive (e.g. ‘‘very good’’) or

negative (e.g. ‘‘badly written’’). Cognitive comments were categorized as (1) identify

problem; (2) suggestion; (3) explanation; and (4) comment on language. The first author

and a research assistant coded the work independently with an inter-rater reliability of

.83. Comments that were neither cognitive nor affective (e.g. ‘‘can’t read your project,

cannot comment’’) were classified as ‘other’ and were later excluded as there were very

few of these.

Fig. 2 Screenshot of peer assessment on iLAP

264 J. Lu, N. Law

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Student grading and assessment activities were coded, quantified, and summed for all

five subtasks. Data from the five classes were collapsed for analysis because: (1) students

from the five classes did not differ significantly in their academic performances in general

based on the achievement data collected from the previous semester; (2) the materials and

tasks on iLAP were identical for all five classes; (3) the five classes did not differ

Fig. 3 Screenshot of the teacher’s view of student assessment

Table 1 Coding scheme for peer feedback

Categories Definition Example

Cognitive

Identifying problems Addressing specific issue ordimension of the essay

You don’t have enough survey questions!

Suggestions A method is suggested to dealwith the problem

‘‘The topic is too broad, should narrowdown the topic’’

Explanation Explanation or elaboration on theproblems identified orsuggestions provided

Your project lacks of reliable evidence.If you don’t collect people’s opinion,how can you know it (subway) isconvenient?

Language Comments addressing the writingin general

Your writing is to colloquial.

Affective

Negative Give criticism ‘‘You didn’t even do it!’’‘‘Badly written’’

Positive Praise the work ‘‘Very good’’‘‘Quite good overall’’

Online peer assessment 265

123

significantly in their final project scores based on the ANOVA test results (F = 1.46,

P [ 0.05); and (4) multilevel intra-class correlation on class dependence (Raudenbush and

Bryk 2002) was 0.01, which indicating no statistically significant variance difference

among the five classes. This implied that teachers’ differences were not a factor.

Statistical analyses were used to explore relationships between students’ online

assessment activities and LS performance. Hierarchical multiple regression was used to

examine the relationship of these elements with project performance (final project scores)

controlled by student grades in previous Computer Literacy and Humanities courses prior

to the commencement of the study. By entering blocks of independent variables in

sequence, we identified additional variance explained by newly introduced variables in

each step. In the first step, two control variables, examination scores in the Humanities and

Computer Literacy courses, were entered into the regression equations. Peer grading

measures to assessees and by assessors were entered in the second step. Peer feedback

measures to assessees and by assessors were entered in the third step. The independent

predictor variables included in this study are the number of grades given to and received

from peers, number of suggestions given to and received from peers, number of expla-

nations given to and received from peers, number of comments on identifying the problems

given to and received from peers, number of comments on language issues given to and

received from peers, number of items of positive and negative emotional feedback given to

and received from peers.

Results

Summary statistics

Data from 181 students was included in the analysis. Data from nine other students was

excluded because their final project scores were missing and they were not involved in any

assessment activities. Table 2 summarizes students’ online peer assessment activities.

Table 3 provides the correlations of all variables. According to Table 2, students on

average gave and received similar amounts of the same types of feedback. Note that the

most frequently given and received types of cognitive feedback were Identifying problem

[Mean (given) = 5.84, Mean (received) = 5.83], followed by Suggestion [Mean

(given) = 1.10, Mean (received) = 1.12], then Comment on language use [Mean

(given) = .71, Mean (received) = .70], and Explanation [Mean (given) = .52, Mean

(received) = .55]. The number of peer grading received by participants (mean = 13.19)

was similar to the number they gave to their peers (mean = 13.94). The variance of

assessment activities also varies across different types of feedback. The higher the range

and means of peer assessment variables the larger the variance. However, within the same

type of feedback, variances between giving and receiving feedback are about the same.

Multiple regression

The multiple squared correlation coefficient was .128 indicating that students’ online

collaboration and evaluation activities accounted for about 12.8% of project performance

variance. Table 4 shows that the two control variables were significant predictors of final

project scores (DR2 = 0.037, p \ 0.05). Students’ performance in the Humanities course

(PHS) was a significant predictor of final project scores (t = 2.58, p \ 0.05) while their

performance in the Computer Literacy course (PCLS) was not (t = -.27, p [ .05). Adding

266 J. Lu, N. Law

123

the number of peer grading received or given to the model produced no significant change

in variance, while adding peer feedback activities did (adjusted DR2 = .142, p \ .01).

Controlling for students’ prior Humanities scores, Computer Literacy scores, and number

of grading, we identified three variables with significant contributions to the model: Giving

suggestions to peers (t = 2.17, p \ .05), giving feedback on identifying problems to peers

(t = 2.16, p \ .05), and positive affective feedback from peers (t = 2.25, p \ .05).

However, the strength of prediction of Humanities performance became insignificant when

peer feedback activities were added (t = .75, p [ .05). Other online assessment activities

were not significant predictors of the final project scores.

Discussion

This study examined whether the involvement of high school students in online peer

assessment predicted their performance on LS projects. We focused on peer grading and

peer cognitive and affective feedback, and their effects on both assessors and assessees.

Online peer assessment was found to significantly affect the quality of students’ project

learning outcomes, probably because it provided opportunities to evaluate the work of

others. We discuss the effects of grading, followed by cognitive and affective feedback.

Table 2 Descriptive statistics of major variables (N = 181)

Min Max Mean S.D.

1 SugTP 0 9 1.10 1.76

2 SugBP 0 8 1.12 1.56

3 ExpTP 0 4 .52 .84

4 ExpBP 0 5 .55 .87

5 IdeTP 0 17 5.84 4.06

6 IdeBP 0 14 5.83 3.13

7 LanTP 0 13 .71 1.39

8 LanBP 0 6 .70 1.16

9 NETP 0 9 .52 1.09

10 NEBP 0 10 .54 1.15

11 PETP 0 23 3.26 3.33

12 PEBP 0 13 3.33 2.71

13 TGTP 0 27 13.98 5.74

14 TGBP 0 26 13.34 4.98

15 PCLS 18 90 68.64 10.92

16 PHS 20.45 81.36 50.83 11.14

17 PS 0.7 13.08 5.81 2.65

SugTP suggestions to peers, SugBP suggestions from peers, ExpTP explanations feedback to peers, ExpBPexplanations from peers, IdeTP feedback on identifying problems to peers, IdeBP feedback on identifyingproblems by peers, LanTP giving feedback on language (sues), LanBP receiving feedback on language(sues), NETP Negative emotional feedback to peers, NEBP Negative emotional feedback from peers, PETPPositive emotional feedback to peers, PEBP Positive emotional feedback from peers, TGTP Total gradesgiven to peers, TGBP Total grades from peers, PCLS Previous Computer Literacy score, PHS PreviousHumanities score, PS project score as the dependent variable

Online peer assessment 267

123

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268 J. Lu, N. Law

123

Effects of peer grading

Although a modest significant correlation was found, the number of peer assigned or

received grades did not predict LS project scores. It was hypothesized that more grading

would improve assessors’ understanding of the rubric and consequently their own per-

formance. Though students gave and received more grades than feedback, the effect of the

former was less significant than that of the latter. This finding echoes studies that suggest

peer grading alone is less effective than peer grading plus feedback. Xiao and Lucking

(2008) found that students involved in peer grading plus qualitative feedback were more

satisfied and showed greater improvements in their writing task performance than those

involved only in peer grading. Ellman (1975) even suggests that peer grading is an

unfortunate reinforcement of the already overemphasized practice of grading. Thus, pro-

viding meaningful, constructive, and concrete comments to fellow students appears to be

more conductive to learning than solely assigning grades.

Effects of cognitive and affective comments

Different types of cognitive comments

Although research findings on the effects of different types of feedback are readily

available in the literature, the different effects these have on assessors and assessees are

not (Li et al. 2010). Moreover, while peer feedback is a common practice in schools

Table 4 Hierarchical multiple regression analysis in predicting project score (N = 187)

R2 Radj2 DR2 DF b t

Step 1 .047* .037* .037* 4.412

PCLS -.02 -.27

PHS .22** 2.89

Step 2 .050 .033 .002 .428

PCLS -.03 -.39

PHS .21** 2.63

TGTP .05 0.65

Step 3 .191* .128* .142* 2.93

PCLS -.05 -.60

PHS .07 .75

TGTP -.04 -.49

SugTP .20* 2.17

IdeTP .20* 2.16

PEBP .16* 2.25

PCLS Previous Computer Literacy score, PHS Previous Humanities score, TGTP Total grades given topeers, SugTP suggestions to peers, IdeTP feedback on identifying problems to peers, PEBP Positiveemotional feedback from peers

* p \ .05

** p \ .01

Online peer assessment 269

123

and is thought to be helpful, there is little agreement as to which types of feedback are

most effective. In this study, we find that students benefit more as assessors than

assessees, particularly with regard to comments that identify problems and make

suggestions.

The more problems assessors identified, and the more suggestions they made, the better

they performed in their own LS projects. It seems reasonable that identifying problems and

making suggestions leads assessors to engage in activities with higher cognitive demands.

Identifying problems, the type of cognitive feedback most frequently given, varied from

such specific comments as ‘‘the photos are not clear’’ to general comments such as ‘‘the

inquiry problem should be clearer’’. Suggestions were the next most frequently given type

of cognitive feedback. Assessors develop clearer and deeper understandings of assessment

criteria and project tasks by judging and commenting on the quality of peer projects (Liu

et al. 2001; Reuse-Durham 2005). They gained insights into their own projects by assessing

the projects of peers (Bostock 2000). Our findings replicated Chen and Tsai’ research

(2009) on in-service teachers in that the more peer feedback given to peers, the more likely

students were to make improvements to their research proposal. Giving explanation and

feedback on language use were not found to be significant predictors of project perfor-

mance. This may be due to the low frequencies of these two kinds of feedback. Future

studies should aim to encourage students to employ these forms of feedback and to study

their effects on learning performance.

We found that cognitive feedback did not have the same effects on assessees, which

contradicts Gielen et al.’s (2009) finding of a positive relationship between receiving

constructive comments, especially justified ones, and learning. Many researchers have

observed that constructing feedback that is useful to assessees is a very complex issue.

The difference between the correlations of research project scores with feedback given

and feedback received, respectively, indicate that those who tend to give more feedback

gain more from the project process at the same time, while weak students do not benefit

from receiving more feedback. In other words, feedback affects assessors rather than

assessees, and peer feedback may not help weak students. This could be because weak

students have difficulties understanding, interpreting, and consequently integrating the

feedback received. Secondly, not all feedback is equally useful. A review of the relevant

literature reveals that different types of feedback may have different effects. Our findings

also showed that the feedback students gave and received was not equally distributed. The

third issue is feedback implementation—the intermediate step that occurs between

feedback and performance improvement (Nelson and Schunn 2009). Simply put, feedback

is useful to recipients only when they act on it (Topping 1998). Thus, feedback affects

assessees indirectly through the mediation of understanding (Nelson and Schunn 2009).

When learners receive feedback, they first need to fully comprehend the problem(s) pin-

pointed or suggestion(s) offered. Unfortunately, explanations were rarely provided by

students in this study (the means were .52 for assessors and .55 for assessees). This could

make it more difficult for the weaker students to benefit from peer feedback as they did

not know how to improve their work when receiving qualitative feedback without suf-

ficient explanation. Another important issue is perceived validity (Straub 1997). Assessees

have to evaluate the usefulness of feedback. They may only act on feedback that they

perceive as valid and useful for improving their performance. Or they may value only

feedback from teachers rather than peers. The fact that so many factors can influence the

extent to which assessees accept and implement feedback helps to explain why cognitive

feedback failed to enhance their performance.

270 J. Lu, N. Law

123

Explanations and language comments did not predict final project performance. Stu-

dents gave very little feedback of this type, and consequently received few of them. In

addition to the low frequencies of occurrence, language comments may have failed to

predict project performance because they were too general, such as ‘‘you should improve

your grammar,’’ or perhaps because language played a minor role in final project

performance.

Affective comments

Positive affective feedback was a significant predictor for the performance of assessees on

LS projects in this study. The more positive comments students received, the more likely

they were to perform well on their projects. Evidence in the literature for the usefulness of

praise is conflicting and inconclusive. Some researchers have found that praise has a

negative affect (Cho and Cho 2010), or no affect, on student performance (Crooks 1988;

Ferris 1997), while others have found that it has a positive effect on student performance

(Straub 1997; Tseng and Tsai 2007). Henderlong and Lepper (2002) found that praise

enhances intrinsic motivation and maintains interest when it encourages performance

attributions, promotes a sense of autonomy, and provides positive information about

personal competence. Affective comments that provoke positive feelings help boost stu-

dent interest, motivation, and self-efficacy, even when they are not task-focused or

informative. Another possible explanation is that the affective language used in feedback

might color a person’s perception of the reviewer and the feedback received (Nelson and

Schunn 2009). Affective comments provoking positive emotions in learners could lead to a

favorable outlook on the comments and consequent implementation. This study found that

students receiving positive feedback were more likely to perform better on their final LS

projects than those who did not. However, no information was collected asking how

assessees interpreted this positive feedback and how such feedback affected their learning

performance. Future study should investigate how such feedback might affect their

learning outcomes. Praise did not have positive effects on assessors, which contradicts the

finding by Cho and Cho (2010). This could be a result of the different method of coding

used within this study: we coded praise and cognitive feedback separately, while Cho and

Cho coded them together.

Prior knowledge

Two control variables: Prior Humanity Scores (PHS) and Previous Computer Literacy

Scores (PCLS), affected students’ LS project performance differently. PCLS was not

significantly related to LS projects scores, perhaps because the computer skills needed for

LS projects were minimal and below the levels of variance. Student project performance

had a low correlation with PHS. Although the three topics: ‘‘consumer education’’,

‘‘transportation and city development’’, and ‘‘economy’’ were related to topics in the

previous Humanities course, they made only a small contribution to Project scores (3.7%

variance change). The fact that PHS became insignificant when cognitive feedback was

added to the model implied great overlap of variance on the two variables. Effects of PHS

were transferred to or represented by the impact of cognitive feedback activities. This

suggests that students who were good at Humanities also made more cognitive feedback.

However, cognitive feedback also had its unique and significant contributions to project

scores, as exemplified by the significant change of variance of 14.2%.

Online peer assessment 271

123

Conclusion

This study expands our understanding of the relationship between peer assessment and

learning performance. It proposes a possible explanation for the benefits of online peer

assessment on student learning performance. Students benefit more as assessors than as

assessees. Delving into how peer grading and feedback influence assessors and assessees,

respectively, three variables stand out. Thoughtful feedback on specific problems and

suggestions are strong predictors of how assessors perform on their final project. Positive

affective feedback predicts a higher performance of assessees. Peer grading behaviors have

very limited effects on the learning performance of both assessors and assessees.

Our findings have several implications for teachers and educational researchers inter-

ested in designing and implementing peer assessment. First, teachers need to be sensitive to

the fact that peer assessment works differently for assessors and assessees. Different types

of peer assessment, either peer grading or peer feedback, set in motion different learning

processes on the part of assessors and assesses, which can lead to different outcomes.

Second, modeling or training should be provided prior to or during the task because peer

assessment is not easy. Teachers should ask students to be specific in their feedback,

particularly with regard to the problems in assessees’ work, and to provide suggestions.

The findings of our study could assist teachers in developing strategies to amplify the

effectiveness of peer assessment for all students. Peer grading appears to be less effective

than peer feedback for assessors, because giving feedback to peers activates crucial cog-

nitive processes that contribute to learning gains of assessors. Students should be

encouraged to give thoughtful and meaningful comments rather than simply assign grades

to peers. Students can be asked to explain why they assigned particular grades to peers.

Third, teachers should scaffold assessment processes with scaffolding tools, particularly

for the weak students. As weak students could neither give nor benefit from qualitative

feedback they need to be given specific instructions on the types of feedback they should

give to peers; they should also be encouraged to reflect on and implement the feedback

they receive. Fourth, students should be encouraged to exchange affective comments that

give socio-emotional support to peers and recognize peers’ achievement. Our results

suggest that positive affective comments are not just about making other people feel good.

They can help boost the motivation, interest, and self-efficacy of assessees, which in turn

can enhance their performance.

Enhancing the effects of feedback on assessees is a complex issue. Effects on assessees

may be hard to see since we might need to see how they change their attitudes with respect

to feedback for it to be successful. For example, students should be asked to respond to

feedback by explaining why (or why not) and how they would implement suggestions for

their work. In future studies, we suggest using mixed methods to investigate how peer

assessment affects different kinds of participants and under what conditions or circum-

stances. Such studies can inform practitioners and researchers about how to cultivate

‘‘mindful reception’’ of feedback (Bangert-Drowns et al. 1991) on the part of assessees.

Acknowledgments This research was supported by General Research Fund (GRF) and Quality EducationFunding (QEF) from Hong Kong government to the authors. We would like to thank the research assistanceof Dr. Deng Liping and the support from our colleagues at the Centre for Information Technology inEducation (CITE) of the University of Hong Kong: Dr Lee Yeung, Dr Lee Man Wai, Mr Andy Chan, andMr Murphy Wong for their technical support and assistance in data collection. We also want to thank theteachers involved in this study: Miss Kwok Siu Mei, Mr Lai Ho Yam, Mr Lo Ching Man. Their enthusiasmfor applying technology in LS motivates us to run this study.

272 J. Lu, N. Law

123

Open Access This article is distributed under the terms of the Creative Commons Attribution Noncom-mercial License which permits any noncommercial use, distribution, and reproduction in any medium,provided the original author(s) and source are credited.

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